ArticleFrontiers in neuroinformatics2018
BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python.
Article in Frontiers in neuroinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Recovery from disorders of consciousness: Lesions and GABAergic modulation in a biologically inspired spiking neural network.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- DeltaQ: Value-Guided Hebbian Learning in Spiking Neuronal Networks for Multi-Goal Navigation.bioRxiv : the preprint server for biology · 2026Article
- Differentiable simulation expands frontiers for biophysical neural models.Nature methods · 2025Article
- Reconfigurable neuromorphic functions in antiferroelectric transistors through coupled polarization switching and charge trapping dynamics.Nature communications · 2025Article
- A Comparative Study of Network-Based Machine Learning Approaches for Binary Classification in Metabolomics.Metabolites · 2025Article
- Exploring spiking neural networks for deep reinforcement learning in robotic tasks.Scientific reports · 2024Article
- Hardware implementation of memristor-based artificial neural networks.Nature communications · 2024Review
- On computational models of theory of mind and the imitative reinforcement learning in spiking neural networks.Scientific reports · 2024Article
- Direct training high-performance deep spiking neural networks: a review of theories and methods.Frontiers in neuroscience · 2024Review
- Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN.Frontiers in neuroscience · 2024Article
- Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch.Frontiers in neuroinformatics · 2024Article
- Analog monolayer SWCNTs-based memristive 2D structure for energy-efficient deep learning in spiking neural networks.Scientific reports · 2023Article
- SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence.Science advances · 2023Article
- From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?Brain sciences · 2023Review
- Unsupervised Spiking Neural Network with Dynamic Learning of Inhibitory Neurons.Sensors (Basel, Switzerland) · 2023Article
- BrainCog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and brain simulation.Patterns (New York, N.Y.) · 2023Article
- EdgeMap: An Optimized Mapping Toolchain for Spiking Neural Network in Edge Computing.Sensors (Basel, Switzerland) · 2023Article
- SNS-Toolbox: An Open Source Tool for Designing Synthetic Nervous Systems and Interfacing Them with Cyber-Physical Systems.Biomimetics (Basel, Switzerland) · 2023Article
- Selective area doping for Mott neuromorphic electronics.Science advances · 2023Article
- Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.Frontiers in neuroinformatics · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithms. Existing software frameworks support a wide range of neural functionality, software abstraction levels, and hardware devices, yet are typically not suitable for rapid prototyping or application to problems in the domain of machine learning. In this paper, we describe a new Python package for the simulation of spiking neural networks, specifically geared toward machine learning and reinforcement learning. Our software, called BindsNET, enables rapid building and simulation of spiking networks and features user-friendly, concise syntax. BindsNET is built on the PyTorch deep neural networks library, facilitating the implementation of spiking neural networks on fast CPU and GPU computational platforms. Moreover, the BindsNET framework can be adjusted to utilize other existing computing and hardware backends; e.g., TensorFlow and SpiNNaker. We provide an interface with the OpenAI gym library, allowing for training and evaluation of spiking networks on reinforcement learning environments. We argue that this package facilitates the use of spiking networks for large-scale machine learning problems and show some simple examples by using BindsNET in practice.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.